@inproceedings{chakravarthi-etal-2022-overview-shared,
title = "Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion",
author = "Chakravarthi, Bharathi Raja and
Muralidaran, Vigneshwaran and
Priyadharshini, Ruba and
Cn, Subalalitha and
McCrae, John and
Garc{\'\i}a, Miguel {\'A}ngel and
Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and
Valencia-Garc{\'\i}a, Rafael and
Kumaresan, Prasanna and
Ponnusamy, Rahul and
Garc{\'\i}a-Baena, Daniel and
Garc{\'\i}a-D{\'\i}az, Jos{\'e}",
editor = "Chakravarthi, Bharathi Raja and
Bharathi, B and
McCrae, John P and
Zarrouk, Manel and
Bali, Kalika and
Buitelaar, Paul",
booktitle = "Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.ltedi-1.58",
doi = "10.18653/v1/2022.ltedi-1.58",
pages = "378--388",
abstract = "Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection is focussed on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022) organised as a part of ACL 2022. The participants were provided with annotated training {\&} development datasets and unlabelled test datasets in all the five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes. The performances of the systems submitted by the participants were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available",
}
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<abstract>Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection is focussed on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022) organised as a part of ACL 2022. The participants were provided with annotated training & development datasets and unlabelled test datasets in all the five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes. The performances of the systems submitted by the participants were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available</abstract>
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%0 Conference Proceedings
%T Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion
%A Chakravarthi, Bharathi Raja
%A Muralidaran, Vigneshwaran
%A Priyadharshini, Ruba
%A Cn, Subalalitha
%A McCrae, John
%A García, Miguel Ángel
%A Jiménez-Zafra, Salud María
%A Valencia-García, Rafael
%A Kumaresan, Prasanna
%A Ponnusamy, Rahul
%A García-Baena, Daniel
%A García-Díaz, José
%Y Chakravarthi, Bharathi Raja
%Y Bharathi, B.
%Y McCrae, John P.
%Y Zarrouk, Manel
%Y Bali, Kalika
%Y Buitelaar, Paul
%S Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F chakravarthi-etal-2022-overview-shared
%X Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection is focussed on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022) organised as a part of ACL 2022. The participants were provided with annotated training & development datasets and unlabelled test datasets in all the five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes. The performances of the systems submitted by the participants were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available
%R 10.18653/v1/2022.ltedi-1.58
%U https://aclanthology.org/2022.ltedi-1.58
%U https://doi.org/10.18653/v1/2022.ltedi-1.58
%P 378-388
Markdown (Informal)
[Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion](https://aclanthology.org/2022.ltedi-1.58) (Chakravarthi et al., LTEDI 2022)
ACL
- Bharathi Raja Chakravarthi, Vigneshwaran Muralidaran, Ruba Priyadharshini, Subalalitha Cn, John McCrae, Miguel Ángel García, Salud María Jiménez-Zafra, Rafael Valencia-García, Prasanna Kumaresan, Rahul Ponnusamy, Daniel García-Baena, and José García-Díaz. 2022. Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 378–388, Dublin, Ireland. Association for Computational Linguistics.